Gastos Casa
05 septiembre, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 20/7/2022 | VTR | 21990 | Andrés | NA |
| 21/7/2022 | Comida | 24660 | Andrés | NA |
| 23/7/2022 | Enceres | 14315 | Andrés | NA |
| 23/7/2022 | Comida | 22263 | Andrés | NA |
| 20/7/2022 | Comida | 41830 | Andrés | NA |
| 25/7/2022 | Comida | 61470 | Tami | NA |
| 25/7/2022 | Comida | 16100 | Tami | NA |
| 25/7/2022 | Cortina baño | 29120 | Tami | NA |
| 28/7/2022 | Electricidad | 78798 | Andrés | NA |
| 29/7/2022 | Netflix | 8320 | Tami | NA |
| 30/7/2022 | Comida | 36170 | Tami | NA |
| 31/7/2022 | Parafina | 22060 | Tami | NA |
| 1/8/2022 | Comida | 11670 | Andrés | NA |
| 8/8/2022 | Comida | 17890 | Tami | NA |
| 8/8/2022 | Comida | 41390 | Tami | NA |
| 19/8/2022 | VTR | 21990 | Andrés | NA |
| 18/8/2022 | Comida | 21860 | Andrés | NA |
| 19/8/2022 | Comida | 5213 | Andrés | NA |
| 22/8/2022 | Parafina | 23300 | Tami | NA |
| 24/8/2022 | Comida | 57780 | Tami | NA |
| 26/8/2022 | mantencion toyotomi | 34000 | Andrés | mantencion toyotomi |
| 27/8/2022 | Comida | 19410 | Tami | NA |
| 29/8/2022 | Netflix | 8320 | Tami | NA |
| 31/8/2022 | Incoludido | 21000 | Tami | NA |
| 31/8/2022 | Electricidad | 89272 | Andrés | PAC ENEL 01686518 |
| 31/8/2022 | Enceres | 12000 | Andrés | Visita gasfiter |
| 3/9/2022 | Comida | 59225 | Andrés | Lider |
| 3/9/2022 | Comida | 21350 | Andrés | Laflordeloto.cl |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.2111e+08 2 4.69 0.0096 **
## lag_depvar 7.6088e+10 1 1694.80 <2e-16 ***
## Residuals 2.1684e+10 483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 879.2301 13578.45 0.020968
## 2-0 27486.070 21622.0658 33350.07 0.000000
## 2-1 20257.232 16696.7389 23817.72 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 331 49720.33 16174.470
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2069.082284 4061.881487 -559.388162 2419.592250 -3011.783489
## 7 8 9 10 11
## 503.425056 -5678.374750 -1162.264055 -3937.929318 -362.934672
## 12 13 14 15 16
## -4892.522266 -1529.186846 -820.029689 450.517738 -3186.921001
## 17 18 19 20 21
## -304.967518 -2067.584973 6672.996042 -1531.988346 -1201.861068
## 22 23 24 25 26
## 1487.284853 -1194.033624 234.019120 1687.798008 -7127.865186
## 27 28 29 30 31
## 983.157804 8210.691094 357.514192 -75.171437 -2458.384104
## 32 33 34 35 36
## 1542.393094 4524.594798 1040.278592 2301.357153 -1972.250410
## 37 38 39 40 41
## 4528.857348 4257.122028 -2354.467103 -3030.540722 -1127.118416
## 42 43 44 45 46
## -10746.859947 7378.990184 2570.883126 1355.812149 8083.090075
## 47 48 49 50 51
## 595.004645 6442.699630 6581.250684 -6058.255974 -4897.625652
## 52 53 54 55 56
## -5107.071165 -7925.654534 6201.941692 -4068.037684 -4851.251154
## 57 58 59 60 61
## 3936.238853 923.700849 -9.001493 162.314964 -4980.521161
## 62 63 64 65 66
## 18184.382006 3530.699009 -3774.660909 5844.881531 7221.150650
## 67 68 69 70 71
## 14466.454769 1413.167275 -13472.578768 -1416.815135 4558.084329
## 72 73 74 75 76
## -5016.189313 -4462.810858 -10509.707630 2548.784800 -5350.121162
## 77 78 79 80 81
## 1154.710905 -6796.421074 668.488937 -2253.414698 -2584.862225
## 82 83 84 85 86
## -3813.007819 -399.060398 2440.112736 3851.523887 520.814283
## 87 88 89 90 91
## -450.819874 229.960620 4328.929498 -1177.938548 1147.721379
## 92 93 94 95 96
## -2077.781904 -1038.009258 191.956658 285.397524 -7477.571077
## 97 98 99 100 101
## 2463.466731 -8561.335240 -2828.469114 -3915.516323 -1592.642289
## 102 103 104 105 106
## -1120.099978 3315.442644 -2253.035586 2692.310367 -1095.872131
## 107 108 109 110 111
## 1035.064293 2634.591263 -3136.208934 -4679.491423 -770.552282
## 112 113 114 115 116
## 1980.436190 11743.538126 -1303.100777 2626.346085 4201.971809
## 117 118 119 120 121
## 3411.352621 -1211.116779 -4803.983712 -3759.079191 2322.007727
## 122 123 124 125 126
## -1751.560002 1339.013644 8844.836542 756.463619 43.447320
## 127 128 129 130 131
## -2598.727848 2609.481303 6988.834155 893.897429 -8612.269448
## 132 133 134 135 136
## 1725.715556 4099.145689 -3232.764248 -1452.090029 -869.960256
## 137 138 139 140 141
## -3886.698902 1211.348633 -481.555026 -2897.350419 1757.926306
## 142 143 144 145 146
## -1861.913494 -7796.175888 2137.608895 -3412.400598 2191.587838
## 147 148 149 150 151
## -198.451913 1076.495515 -322.062800 1387.532337 1205.109614
## 152 153 154 155 156
## 3361.795089 -4887.425100 -1154.051345 -3207.890778 6009.502636
## 157 158 159 160 161
## 9739.487857 -3084.980459 -4414.224117 3995.182312 534.959561
## 162 163 164 165 166
## 3021.982486 -5619.844742 -6410.469385 4541.730598 17721.464228
## 167 168 169 170 171
## 3781.503871 -269.129467 -2300.396378 -925.408248 3785.759019
## 172 173 174 175 176
## -62.151887 -7899.497796 3120.932730 4552.261892 812.025623
## 177 178 179 180 181
## 8936.028769 -9139.452087 -3261.959399 -10500.581708 -10898.515591
## 182 183 184 185 186
## 1666.392361 9687.252963 -1144.317123 6219.627713 6781.204238
## 187 188 189 190 191
## 13319.876003 8471.847740 -4083.969923 2508.536208 10406.656470
## 192 193 194 195 196
## -1686.684646 -2443.178675 -10232.160933 -6197.672209 1464.883770
## 197 198 199 200 201
## -5017.124451 -9530.453170 5736.990283 -2783.905468 -1407.244763
## 202 203 204 205 206
## -493.864408 6800.089484 10110.928294 705.001433 3053.687465
## 207 208 209 210 211
## 3206.322114 5873.117266 12878.971917 -5749.282104 -11269.814597
## 212 213 214 215 216
## -5507.121002 -10366.422114 -4751.435250 1886.855981 -12683.420179
## 217 218 219 220 221
## 16830.472972 8060.234016 1695.529448 26844.365477 12421.242759
## 222 223 224 225 226
## 7141.220329 13807.102823 -4222.124041 -1948.809105 3636.775645
## 227 228 229 230 231
## 224.345219 2649.450095 8919.270809 5694.575158 -2053.780152
## 232 233 234 235 236
## -1911.074656 9397.757532 -11599.410170 -7223.983543 -8391.733854
## 237 238 239 240 241
## -9860.871923 3413.596989 1642.266766 -8030.058435 -8647.603066
## 242 243 244 245 246
## 9509.303724 -7466.090216 2852.291501 -9980.160499 -3644.803361
## 247 248 249 250 251
## 1847.113710 1386.288568 -11964.760563 4098.818695 2450.417586
## 252 253 254 255 256
## 4555.932239 2418.710859 -908.802621 11395.624211 21016.710694
## 257 258 259 260 261
## 3125.366515 -4345.185522 4110.179844 -1713.245051 3760.794936
## 262 263 264 265 266
## -4846.137495 -10816.310614 -4526.682265 -272.905397 -4941.211334
## 267 268 269 270 271
## 9070.938936 -4091.275125 4421.358569 -1924.610064 4634.453816
## 272 273 274 275 276
## 863.540354 7452.722734 -1335.590689 12127.697277 -4600.550626
## 277 278 279 280 281
## 1778.431666 -323.647615 7918.496572 -5061.116576 -2659.657578
## 282 283 284 285 286
## -11147.402550 -2426.055485 18920.062304 7838.566031 2719.520595
## 287 288 289 290 291
## -650.348249 914.273595 6416.424812 6849.880948 -18854.847157
## 292 293 294 295 296
## -10983.648681 -7838.189794 10028.373392 3310.351309 -979.569868
## 297 298 299 300 301
## 27613.865432 9971.051325 4727.924747 9333.302977 2610.595515
## 302 303 304 305 306
## -1258.186680 7731.966573 -24505.022732 -3422.018312 -10.707827
## 307 308 309 310 311
## -6795.713000 -3715.528620 3228.995328 -8937.658957 -2875.015679
## 312 313 314 315 316
## -7809.348429 2018.141422 -2743.035044 2470.164734 -3708.567259
## 317 318 319 320 321
## 27846.462473 -680.229182 3359.703369 10874.888826 5526.155452
## 322 323 324 325 326
## 32283.624043 4678.879309 -21349.852838 1696.068977 1030.337885
## 327 328 329 330 331
## -6522.983222 -1684.987215 -33178.321095 1408.257942 -1814.655032
## 332 333 334 335 336
## 396.943763 -2701.780695 4565.980729 -29.952482 -6557.072783
## 337 338 339 340 341
## -2654.312468 -1715.528534 -7202.088953 4395.115005 -907.090652
## 342 343 344 345 346
## -1281.567963 -540.144513 619.331612 900.464187 -1224.845424
## 347 348 349 350 351
## -9050.482774 -12718.861549 2930.127464 -3774.234573 -3092.628157
## 352 353 354 355 356
## -5406.969208 2357.636418 1927.576117 3243.772396 -3339.626813
## 357 358 359 360 361
## -66.985213 1107.555860 7412.462920 570.209174 244.724027
## 362 363 364 365 366
## 2860.664709 -2509.105425 -602.079835 -8460.248313 -4237.564452
## 367 368 369 370 371
## -5781.464271 -4461.323669 -6729.644828 5596.633198 850.537612
## 372 373 374 375 376
## 7566.674329 -7302.302316 -1843.766213 -2959.582740 -2016.450766
## 377 378 379 380 381
## -11998.627339 2492.903985 -10107.428358 6322.698714 9846.268328
## 382 383 384 385 386
## 3486.712311 -2092.591566 1932.093820 7040.148715 11613.514999
## 387 388 389 390 391
## -5742.586612 -5215.452563 63.627193 8785.620360 1927.985948
## 392 393 394 395 396
## 11322.704348 -9908.088949 2893.939459 807.087479 660.724120
## 397 398 399 400 401
## -549.879365 -438.661134 -14345.554989 8862.972853 -956.822731
## 402 403 404 405 406
## -1130.210110 7242.381806 -7763.059805 -1016.043010 -2235.556089
## 407 408 409 410 411
## -5493.285776 -2463.838863 -3498.733605 -8303.187614 6677.075386
## 412 413 414 415 416
## 2071.834592 -6984.152420 -7223.527836 14764.385119 4138.393085
## 417 418 419 420 421
## 4751.471130 -7840.104565 -4439.370995 -2239.873838 3203.735211
## 422 423 424 425 426
## -13677.879148 -2285.650334 -8584.447272 3616.064518 7500.937600
## 427 428 429 430 431
## 6976.071319 -3691.152249 -3778.418630 -4336.387717 -1357.344648
## 432 433 434 435 436
## -5276.504121 -6138.115702 -5402.456295 -803.218891 -278.687802
## 437 438 439 440 441
## -4431.738869 3155.274876 5342.101804 -4646.333170 -1701.216454
## 442 443 444 445 446
## 2037.583606 -3420.358019 3282.876949 -6189.746709 -11652.960854
## 447 448 449 450 451
## -3922.071402 10253.938876 -1586.642403 5204.587058 -5501.579844
## 452 453 454 455 456
## -691.663604 810.153982 3429.222923 -11919.836140 3863.524079
## 457 458 459 460 461
## -6276.054398 7013.698508 3394.967283 2838.008237 -3554.039834
## 462 463 464 465 466
## 2430.438444 295.815315 2092.167809 -248.340429 3629.328216
## 467 468 469 470 471
## -2403.883879 6078.435606 -6741.740098 -2665.960461 -1870.394732
## 472 473 474 475 476
## -4306.138320 3404.925887 8153.390319 -5768.250586 1813.122121
## 477 478 479 480 481
## -5873.697313 -2463.578865 2417.570870 -12563.666620 -9237.527676
## 482 483 484 485 486
## -591.525762 606.676139 -412.577138 -812.467327 -9068.462187
## 487 488
## 11702.039226 6664.905757
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17200.20 20077.12 24375.53 24090.55 26468.50 23773.29 24497.09 19679.41
## 10 11 12 13 14 15 16 17
## 19413.22 16728.22 17513.81 14209.04 14260.74 14932.34 16646.64 14949.11
## 18 19 20 21 22 23 24 25
## 15994.58 15361.58 22517.99 21592.43 21066.86 22976.61 22295.55 22954.92
## 26 27 28 29 30 31 32 33
## 24820.15 18685.13 20429.31 28348.49 28406.74 28076.24 25680.89 27097.98
## 34 35 36 37 38 39 40 41
## 30981.15 31333.21 32757.11 30241.71 34185.88 37427.47 34452.83 31230.40
## 42 43 44 45 46 47 48 49
## 30066.15 20547.30 28144.55 30606.47 31707.05 38616.57 38105.87 42816.75
## 50 51 52 53 54 55 56 57
## 47097.26 39718.91 34230.64 29201.37 22274.20 28629.89 25174.82 21433.76
## 58 59 60 61 62 63 64 65
## 25888.16 27160.86 27460.97 27877.09 23704.90 40469.44 42332.66 37528.98
## 66 67 68 69 70 71 72 73
## 41779.85 46746.83 57526.40 55519.44 40608.53 38088.34 41137.76 35378.38
## 74 75 76 77 78 79 80 81
## 30783.14 21389.50 24624.41 20507.57 22615.42 17457.65 19494.13 18712.58
## 82 83 84 85 86 87 88 89
## 17730.15 15778.92 17070.03 20715.76 25179.61 26179.82 26205.04 26828.21
## 90 91 92 93 94 95 96 97
## 30996.37 29814.71 30824.50 28868.72 28060.19 28432.17 28843.00 22353.39
## 98 99 100 101 102 103 104 105
## 25399.91 18357.61 17201.80 15222.07 15524.96 16209.41 20728.75 19802.69
## 106 107 108 109 110 111 112 113
## 23350.44 23138.22 24831.84 27738.64 25210.63 21616.98 21895.28 24569.18
## 114 115 116 117 118 119 120 121
## 35547.10 33721.08 35577.74 38607.36 40583.69 38247.98 33014.94 29318.14
## 122 123 124 125 126 127 128 129
## 31422.70 29684.70 30878.59 38557.68 38196.41 37248.16 34078.95 35878.74
## 130 131 132 133 134 135 136 137
## 41332.96 40767.41 31877.28 33155.28 36378.34 32751.52 31121.96 30197.41
## 138 139 140 141 142 143 144 145
## 26718.51 28147.70 27914.92 25577.07 27622.63 26233.03 19768.39 22830.54
## 146 147 148 149 150 151 152 153
## 20634.56 23642.74 24188.36 25795.35 25979.32 27650.75 28965.06 32028.85
## 154 155 156 157 158 159 160 161
## 27451.77 26707.03 24236.78 30192.37 41105.41 39418.22 36755.67 41828.33
## 162 163 164 165 166 167 168 169
## 43251.59 46703.13 42121.76 37379.98 42861.82 59334.07 61569.27 59966.82
## 170 171 172 173 174 175 176 177
## 56759.41 55141.96 57872.72 56886.64 49098.35 51951.31 55732.97 55769.54
## 178 179 180 181 182 183 184 185
## 62972.74 53375.96 50093.01 40805.80 32256.89 35801.75 46010.60 45460.94
## 186 187 188 189 190 191 192 193
## 51475.80 57280.70 68176.15 73514.11 67143.04 67338.49 74482.54 70113.89
## 194 195 196 197 198 199 200 201
## 65590.02 54721.67 48689.54 50128.70 45677.45 37764.58 44256.33 42465.24
## 202 203 204 205 206 207 208 209
## 42099.44 42582.77 49447.64 58429.57 58055.31 59798.11 61471.17 65301.89
## 210 211 212 213 214 215 216 217
## 74867.14 66867.39 54933.26 49485.85 40388.29 37314.29 40460.42 30376.53
## 218 219 220 221 222 223 224 225
## 47527.05 54924.18 55835.49 78838.33 86411.49 88435.61 96106.12 86962.67
## 226 227 228 229 230 231 232 233
## 80898.51 80476.08 77091.12 76243.87 81030.28 82408.78 76786.22 71949.24
## 234 235 236 237 238 239 240 241
## 77661.84 64170.41 56123.88 47990.59 39514.69 43750.30 45925.49 39307.89
## 242 243 244 245 246 247 248 249
## 32921.55 43311.23 37498.14 41474.87 33658.09 32350.46 36043.85 38897.19
## 250 251 252 253 254 255 256 257
## 29631.04 35631.01 39472.07 44721.00 47467.66 46954.95 57363.29 75042.92
## 258 259 260 261 262 263 264 265
## 74856.04 68096.96 69594.25 65775.63 67236.85 60929.45 50092.25 46078.19
## 266 267 268 269 270 271 272 273
## 46289.78 42355.92 51251.85 47486.07 51676.04 49772.97 53882.75 54181.85
## 274 275 276 277 278 279 280 281
## 60262.02 57871.59 67645.41 61506.85 61719.08 60050.93 65853.69 59518.80
## 282 283 284 285 286 287 288 289
## 56046.83 45490.20 43870.22 61282.15 66869.91 67283.63 64674.30 63752.15
## 290 291 292 293 294 295 296 297
## 67794.83 71745.85 52544.22 42543.05 36491.63 46920.65 50196.28 49300.99
## 298 299 300 301 302 303 304 305
## 73749.66 79757.08 80431.70 85092.26 83272.04 78250.46 81753.45 56390.45
## 306 307 308 309 310 311 312 313
## 52612.56 52289.00 46014.39 43194.72 46835.66 39310.16 38018.92 32523.72
## 314 315 316 317 318 319 320 321
## 36347.75 35520.55 39392.00 37355.39 63410.80 61229.44 62869.97 70951.56
## 322 323 324 325 326 327 328 329
## 73363.80 99111.41 97472.14 73050.07 71835.38 70175.55 62043.27 59135.46
## 330 331 332 333 334 335 336 337
## 28770.17 32496.23 32940.34 35284.49 34618.45 40445.67 41532.50 36730.46
## 338 339 340 341 342 343 344 345
## 35936.67 36064.66 31334.74 37396.38 38066.71 38327.86 39212.81 41017.39
## 346 347 348 349 350 351 352 353
## 42858.42 42607.48 35478.43 25947.73 31348.23 30197.34 29783.11 27374.65
## 354 355 356 357 358 359 360 361
## 32102.42 35895.94 40406.20 38576.27 39849.73 42010.54 49483.08 50039.42
## 362 363 364 365 366 367 368 369
## 50243.19 52732.11 50189.22 49627.96 42196.28 39363.75 35500.75 33256.22
## 370 371 372 373 374 375 376 377
## 29272.80 36636.89 38947.75 46915.73 40824.34 40265.73 38787.74 38315.63
## 378 379 380 381 382 383 384 385
## 29087.81 33734.00 26713.02 35018.30 45459.43 49062.16 47317.48 49329.99
## 386 387 388 389 390 391 392 393
## 55615.20 65199.87 58340.17 52750.52 52476.38 59933.16 60462.01 69221.37
## 394 395 396 397 398 399 400 401
## 58213.06 59796.34 59351.85 58830.31 57301.38 56049.98 42670.03 51345.54
## 402 403 404 405 406 407 408 409
## 50335.50 49290.90 55759.20 48223.61 47527.56 45836.71 41468.70 40287.16
## 410 411 412 413 414 415 416 417
## 38330.76 32363.07 40318.31 43275.30 37891.81 32928.61 47956.04 51841.10
## 418 419 420 421 422 423 424 425
## 55811.53 48201.80 44486.59 43148.69 46772.74 35070.51 34796.88 28995.51
## 426 427 428 429 430 431 432 433
## 34643.92 43058.79 50023.15 46754.70 43792.67 40685.63 40572.65 37013.54
## 434 435 436 437 438 439 440 441
## 33111.46 30316.50 31909.12 33777.88 31761.58 36678.76 42949.33 39667.65
## 442 443 444 445 446 447 448 449
## 39370.56 42408.50 40272.41 44303.75 39500.82 30439.07 29264.35 40740.36
## 450 451 452 453 454 455 456 457
## 40418.56 46129.01 41719.38 42072.70 43710.21 47467.41 37235.48 42135.63
## 458 459 460 461 462 463 464 465
## 37510.87 45159.32 48716.28 51364.33 48059.56 50424.90 50628.55 52393.91
## 466 467 468 469 470 471 472 473
## 51886.24 54860.88 52161.14 57265.31 50454.53 48040.39 46611.71 43200.65
## 474 475 476 477 478 479 480 481
## 46996.18 54537.82 48906.31 50627.41 45361.58 43723.57 46586.24 35889.38
## 482 483 484 485 486 487 488
## 29383.38 31272.32 33997.29 35502.90 36478.89 30052.96 42714.67
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8547
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.690033 0.5602852 2.898266
## t2* 1694.804314 28.6714248 246.696010
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.318466 4.797939 10.65857
## 2 lag_depvar 1346.387305 1707.824305 2154.16083Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 05 01:01:10 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 05 01:01:19 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Sep 05 01:01:28 2022
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## =-=-=-=-= Iteration 12000 Mon Sep 05 01:02:04 2022
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## =-=-=-=-= Iteration 18000 Mon Sep 05 01:02:31 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 5.496500 | 5.70810 | 7.294219 |
| Comida | NA | 285.084375 | 304.77170 | 337.832438 |
| Comunicaciones | NA | 0.000000 | 0.00000 | 0.000000 |
| Electricidad | NA | 48.842750 | 37.25090 | 31.008531 |
| Enceres | NA | 13.849375 | 14.42045 | 23.642281 |
| Farmacia | NA | 2.747500 | 9.49665 | 11.199188 |
| Gas/Bencina | NA | 56.943750 | 30.92770 | 25.801687 |
| Diosi | NA | 16.628375 | 38.26405 | 37.835531 |
| donaciones/regalos | NA | 0.000000 | 8.60410 | 8.584969 |
| Electrodomésticos/ Mantención casa | NA | 5.916000 | 36.32340 | 25.920875 |
| VTR | NA | 27.240000 | 22.34815 | 21.135250 |
| Netflix | NA | 7.607125 | 7.26010 | 7.630281 |
| Otros | NA | 4.726625 | 1.89065 | 1.181656 |
| Total | 0 | 475.082375 | 517.26595 | 539.066906 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1713, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-09-09 00:04:58 sería de: 35.949 pesos// Percentil 95% más alto proyectado: 39.800,62
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34289.70 | 34237.56 |
| Lo.80 | 34527.79 | 34601.46 |
| Point.Forecast | 35948.95 | 38511.90 |
| Hi.80 | 38010.73 | 43191.23 |
| Hi.95 | 39149.62 | 45668.32 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3573 986.4061
## s.e. 0.1481 38.4438
##
## sigma^2 = 27945: log likelihood = -280.18
## AIC=566.35 AICc=566.97 BIC=571.63paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3667 33.4957
## s.e. 0.1437 1.3032
##
## sigma^2 = 27241: log likelihood = -279.63
## AIC=565.26 AICc=565.88 BIC=570.55forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 963.6361 | 635.6049 | 664.3815 |
| Lo.80 | 1083.9894 | 757.0292 | 743.5117 |
| Point.Forecast | 1311.3423 | 986.4050 | 919.6053 |
| Hi.80 | 1538.6951 | 1215.7809 | 1216.7059 |
| Hi.95 | 1659.0484 | 1337.2051 | 1411.0561 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.1
## [7] ggiraph_0.8.3 tidytext_0.3.4 DT_0.24
## [10] autoplotly_0.1.4 rvest_1.0.3 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.17.0 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.2 forcats_0.5.2
## [22] dplyr_1.0.10 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.8 httr_1.4.4
## [34] readxl_1.4.1 zoo_1.8-10 stringr_1.4.1
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0.5 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.40 uuid_1.1-0 rstudioapi_0.14
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.0
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.5.1 fBasics_4021.92 rprojroot_2.0.3
## [25] vctrs_0.4.1 generics_0.1.3 xfun_0.32
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.5.7
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.1
## [37] spatial_7.3-14 timeDate_4021.104 rlang_1.0.5
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.0 selectr_0.4-2
## [46] broom_1.0.1 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.9 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.1 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
## [58] Rcpp_1.0.9 base64enc_0.1-3 fracdiff_1.5-1
## [61] haven_2.5.1 fs_1.5.2 magrittr_2.0.3
## [64] timeSeries_4021.104 lmtest_0.9-40 reprex_2.0.2
## [67] googledrive_2.0.0 mvtnorm_1.1-3 sjmisc_2.8.9
## [70] hms_1.1.2 evaluate_0.16 xtable_1.8-4
## [73] sjstats_0.18.1 ggeffects_1.1.3 compiler_4.1.2
## [76] KernSmooth_2.23-20 crayon_1.5.1 minqa_1.2.4
## [79] htmltools_0.5.3 tzdb_0.3.0 lubridate_1.8.0
## [82] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [85] boot_1.3-28 Matrix_1.3-4 car_3.1-0
## [88] cli_3.3.0 quadprog_1.5-8 parallel_4.1.2
## [91] insight_0.18.2 igraph_1.3.4 pkgconfig_2.0.3
## [94] xml2_1.3.3 bslib_0.4.0 estimability_1.4.1
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_1.0.0
## [100] digest_0.6.29 parameters_0.18.2 janitor_2.1.0
## [103] rmarkdown_2.16 cellranger_1.1.0 curl_4.3.2
## [106] gtools_3.9.3 urca_1.3-3 nloptr_2.0.3
## [109] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
## [112] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.1
## [115] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.12.1 bit_4.0.4
## [121] sass_0.4.2 performance_0.9.2 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))